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Corrosion test improvement by data driven climatic modeling

MATERIALS AND CORROSION-WERKSTOFFE UND KORROSION 2017年第12期68卷 1338-1342页

作者： Mayrhofer, R. Baeck, T. Mercedes Benz Werk Sindelfingen D-71059 Sindelfingen Germany Leiden Univ Leiden Inst Adv Comp Sci Ca Leiden Netherlands Divis Intelligent Solut GmbH Joseph von Fraunhofer Str 20 Dortmund Germany

Many corrosion test experiments, using a range of testing methods and car body materials, are done by the automotive industry worldwide to meet the corrosion requirements in the field. In this paper, we use test data and climate field data for data driven modeling of the test results. One contribution reported here is to find the most influencing factors for corrosion test progress and field correlation. Therefore, iron and zinc panels with automotive paintings conducted in several corrosion tests are measured and transformed into suitable data. The corrosion parameters are defined as factors and the result of corrosion testing (i.e., scribe creepage values) as output variable. With this data, a statistical model is generated and validated. The best fit model is analyzed to find sensible testing parameters effecting the corrosion results. The same procedure is also applied for climatic field data, which is first transformed into characteristics such as humidity, duration of wetness, and salt load. The data from corrosion testing experiments are then mapped to the climatic load in the field. A match to the field correlation can be found, as demonstrated by our results.

关键词： corrosion test data driven modeling field data optimization predictive analytics

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Scalable data-driven modeling of spatio-temporal systems: Weather forecasting

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INTELLIGENT data ANALYSIS 2017年第3期21卷 577-595页

作者： Moshki, Mohsen Kabiri, Peyman Mohebalhojeh, Alireza Iran Univ Sci & Technol Sch Comp Engn Univ RdHengam StResalat Sq Tehran *** Iran Univ Tehran Inst Geophys Tehran Iran

In this paper, a new data-driven method for short-range forecasting of spatio-temporal systems is proposed. It uses NCEP data as raw data to construct forecasting model. The global model consists of several local models. Each local model is constructed in three steps. In the first step, a local dataset is constructed based on NCEP raw data. This dataset is a very high-dimensional data with huge number of redundant and irrelevant features. In the second step, a feature selection method named GRASP is applied on the local dataset and produces a new local dataset whose features are reduced significantly. In the third step, a regression ensemble method called Bagging is used to construct a local model. Both GRASP and Bagging methods are scalable modules with respect to the computational power needed. The proposed method makes it possible to control the trade-off between speed and precision. In addition to the scalability, the proposed method, in some points produces forecasts more precise than the GFS system.

关键词： Feature selection regression ensemble spatio-temporal modeling data driven modeling Numerical Weather Prediction

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data-driven Hydrodynamic modeling for a Flippers-driven Underwater Vehicle-Manipulator System 9

Data-Driven Hydrodynamic Modeling for a Flippers-Driven Unde...

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9th IEEE data driven Control and Learning Systems Conference (DDCLS)

作者： Wang, Yu Bai, Xuejian Cheng, Long Wang, Shuo Tan, Min Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

ISBN: (纸本)9781728159225

In this study, a data-driven hydrodynamic modeling method for a flippers-driven underwater vehicle-manipulator system (F-UVMS) is proposed. The modeling method combines parameter identification technique and computational fluid dynamics simulation, which makes the acquisition of hydrodynamic coefficients efficient and convenient. The hydrodynamic analysis of the biomimetic flipper propulsor is completed and the relationships between the generated forces and the motion parameters of the biomimetic flipper propulsor are revealed. The wake effects are analyzed to reveal the mechanism of the biomimetic flipper propulsor. Lift and drag coefficients are identified by the hydrodynamic data and the dynamic model of the biomimetic flipper propulsor is derived. The proposed hydrodynamic model is applied to the position control strategy of the F-UVMS. The motion experiments of the F-UVMS are conducted in the sea and the motion patterns of the F-UVMS are validated.

关键词： data driven modeling underwater vehicle-manipulator system underwater biomimetic vehicle hydrodynamic modeling

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data driven Fuzzy modeling Using Level Sets

Data Driven Fuzzy Modeling Using Level Sets

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Leite, Daniel Gomide, Fernando Yager, Ronald Adolfo Ibanez Univ Fac Engn & Sci Santiago Chile Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil Iona Coll Inst Machine Intelligence New Rochelle NY 10801 USA

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

The paper looks at the structure of fuzzy rule based models from the point of view of a function relating membership grades of inputs with rule outputs. This view in turn is generalized by an approach that produces the output functions of the fuzzy rules using input and output data. In this view, a formulation to compute the output of the model consists of estimating the parameters of the output functions. Essentially, the paper suggests an alternative method for fuzzy modeling based on output functions constructed from level sets and input and output data. The data driven method provides an easy and efficient way to develop and process fuzzy models. Examples of function estimation problems show that the data driven level set method is very effective when compared with alternative modeling techniques.

关键词： fuzzy modeling data driven modeling

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PHYSICS-BASED AND data-driven modeling OF HYBRID ROBOT MOVEMENT ON SOFT TERRAIN

PHYSICS-BASED AND DATA-DRIVEN MODELING OF HYBRID ROBOT MOVEM...

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作者： Wang, Guanjin University of Maryland

学位级别：博士

Navigating an unmapped environment is one of the ten biggest challenges facing the robotics community. Wheeled robots can move fast on flat surfaces but suffer from loss of traction and immobility on soft ground. However, legged machines have superior mobility over wheeled locomotion when they are in motion over flowable ground or a terrain with obstacles but can only move at relatively low speeds on flat surfaces. A question to answer is as follows: If legged and wheeled locomotion are combined, can the resulting hybrid leg-wheel locomotion enable fast movement in any terrain condition? To investigate the rich physics during dynamic interactions between a robot and a granular terrain, a physics-based computational framework based on the smoothed particle hydrodynamics (SPH) method has been developed and validated by using experimental results for single robot appendage interaction with the granular system. This framework has been extended and coupled with a multi-body simulator to model different robot configurations. Encouraging agreement is found amongst the numerical, theoretical, and experimental results, for a wide range of robot leg configurations, such as curvature and shape. Real-time navigation in a challenging terrain requires fast prediction of the dynamic response of the robot, which is useful for terrain identification and robot gait adaption. Therefore, a data-driven modeling framework has also been developed for the fast estimation of the slippage and sinkage of robots. The data-driven model leverages the high-quality data generated from the offline physics-based simulation for the training of a deep neural network constructed from long short-term memory (LSTM) cells. The results are expected to form a good basis for online robot navigation and exploration in unknown and complex terrains.

关键词： Computational physics Engineering Fluid mechanics data driven modeling Dynamics Granular Material Robot Navigation Smoothed Particle Hydrodynamics Terramechanics

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data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2024年第1期66卷 e33702-e33702页

作者： Piltan, Onur Can Kizilay, Ahmet Belen, Mehmet A. A. Mahouti, Peyman Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkiye Iskenderun Tech Univ Dept Elect & Elect Engn Hatay Turkiye

Horn antenna designs are favored in many applications where ultra-wide-band operation range alongside of a high-performance radiation pattern characteristics are requested. Scattering-parameter characteristics of antennas is an important design metric, where inefficiency in the input would drastically lower the realized gain. However, satisfying the requirement for scattering parameters are not enough for having an antenna with high-performance results, where the radiation characteristic of the design can be changed independently than the scattering parameters behavior. A design might have a high-efficiency performance, but the radiation characteristics might not be acceptable. Furthermore, there are other design considerations such as size and volume of the design alongside of these conflicting characteristics, which directly affect the manufacturing cost and limits the possible applications. In this work, by using data-driven surrogate modeling, it is aimed to achieve a computationally efficient design optimization process for horn antennas with high radiation performance alongside of being small in or within the limits of the desired application limits. Here, the geometrical design variables, operation frequency, and radiation direction of the design will be taken as the input, while the realized gain of the design is taken as the output of the surrogate model. Series of powerful and commonly used artificial intelligence algorithms, including Deep Learning had been used to create a data-driven surrogate model representation for the handled problem, and 80% computational cost reduction had been obtained via proposed approach. As for the verification of the studied optimization problem, an optimally designed antenna is prototyped via the use of three-dimensional printer and the experimental results ware compared with the results of surrogate model.

关键词： artificial intelligence 3D printer data driven modeling deep learning optimization surrogate modeling

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Progress in data-driven thermal comfort analysis and modeling

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ENERGY AND BUILDINGS 2025年 336卷

作者： Culic, Ana Nizetic, Sandro Gambiroza, Jelena Culic Solic, Petar Univ Split FESB Rudjera Boskov 32 Split 21000 Croatia Univ Split Fac Maritime Studies Rudjera Boskov 37 Split 21000 Croatia

Thermal comfort modeling has become increasingly reliant on data-driven approaches, utilizing the potential of machine learning algorithms to predict and improve indoor environmental quality. This paper provides a comprehensive review of methodologies and approaches employed in thermal comfort research, focusing on input variables, output predictions, applied algorithms, and performance evaluation metrics. The study systematically analyzes key environmental inputs, i.e. air temperature, mean radiant temperature, air velocity, and relative air humidity, along with physiological inputs including heart rate and skin temperature which are used to predict thermal comfort indicators, i.e. Thermal Comfort (TC), Thermal Preference (TP), Thermal Sensation (TS) and Predicted Mean Vote (PMV). A variety of machine learning algorithms, such as Random Forest, Support Vector Machine, Artificial Neural Networks, K-Nearest Neighbors have been applied in both regression and classification tasks. The assessment of thermal comfort models developed through the literature was carried out using common performance metrics;Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Accuracy, Precision, Recall and F1-score. The most common algorithms and performance metrics were considered from the reviewed papers and the best results are presented within the herein presented work reflecting the complexity of predicting personal thermal comfort. The paper also highlights the importance of dataset diversity, analyzing data sources from multiple geographical regions, including Europe, North America, and Asia. Finally, the future direction for thermal comfort modeling is discussed, emphasizing the need for developing a framework that can support the widespread application of thermal comfort models in energy-efficient building management systems.

关键词： Thermal comfort data driven modeling Smart buildings

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MTSSF-GRU-FNN based modeling of vehicle lateral and longitudinal coupling dynamics

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年

作者： Zhang, Bowen Zhao, Min Sun, Dihua He, Changchang Tang, Zhongyuan Chongqing Univ Sch Automat Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China

The influence of complex nonlinear factors and the coupling characteristics of the vehicle's lateral and longitudinal directions must be addressed in analyzing and modeling the physical mechanisms of autonomous vehicle dynamics. Otherwise, this will lead to errors between the actual vehicle dynamics and the model when the operating condition changes. This paper proposes a nonlinear proxy modeling method (MTSSF-GRU-FNN) to characterize the complex vehicle dynamics. The Gated Recurrent Units (GRUs) construct a memory-enhanced network that recognizes the continuous variability and strong temporal dependencies inherent in the real-world operation. Moreover, the feedforward neural network (FNN) is introduced in this paper to enhance the fitting performance for multi-input, multi-output nonlinear relationships caused by lateral and longitudinal coupling. Finally, the historical information is integrated into the network using the Multi Time Step State Feature (MTSSF) sliding window method. Simulation results based on a four-wheel dynamic model in the Carsim/Simulink environment under different road adhesion coefficients show the high accuracy and superiority of the proposed method that 0.2595 in average MAE, 0.6482 in average RMSE, and 142.542 ms in Computing time.

关键词： Vehicle dynamics lateral and longitudinal coupling data driven modeling gate recurrent unit feedforward neural network

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Dynamic Calculation Method of Interwell Connectivity Based on the Attention Mechanism

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ENERGY SCIENCE & ENGINEERING 2025年第4期13卷 1807-1818页

作者： Li, Wei Wei, Feng Chen, Xiaoquan Yu, Zhigang Zhou, Zhenyu Zhang, Yuli Ma, Shuai Meng, Qingjun Yang, Chunyi China Univ Geosci Wuhan Adv Res Inst Wuhan Peoples R China Zhanjiang Branch CNOOC China Ltd Zhanjiang Peoples R China Guangdong NanYou Serv Co Ltd Guangzhou Peoples R China Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu Peoples R China

The analysis of interwell connectivity plays a pivotal role in the optimization of the injection-production relationship, the formulation of an oilfield development plan and the description of the remaining oil distribution. Nevertheless, conventional techniques for determining interwell connectivity, such as tracer analysis, interference well testing, and numerical simulation, are characterized by high costs and low efficiency. To address this challenge, an increasing number of artificial intelligence methods have been proposed. However, these methods often necessitate the availability of a substantial quantity of labeled data to facilitate model training, and sensitivity analysis is required to ascertain the interwell connectivity of the model training results. Furthermore, the majority of existing methods are unable to perform dynamic analysis of interwell connectivity. This paper proposes a dynamic calculation method of interwell connectivity based on an attention mechanism to address the aforementioned issues. The interwell connectivity is taken as the intermediate parameter of the neural network, with the interwell connectivity between each injection well and the current production well in the well group inverted by predicting the daily fluid production of each production well in the well group. The experimental results demonstrate that the interwell connectivity calculations presented in this paper are in accordance with the actual operational conditions observed in the oil field and the reservoir expertize of industry professionals. This indicates that our approch can effectively support dynamic decision-making in the production process of oil reservoirs.

关键词： attention mechanism convolutional neural network data driven modeling dynamic interwell connectivity

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NEAMS IRP challenge problem 2: Thermal striping of reactor Internals☆

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NUCLEAR ENGINEERING AND DESIGN 2025年 433卷

作者： Baglietto, Emilio Acierno, John Manera, Annalisa Nguyen, Quynh M. Petrov, Victor Pham, Monica Wang, Yu-Jou Jin, Yue Feng, Jinyong Strasser, Wayne Shaver, Dillon Merzari, Elia MIT Dept Nucl Sci & Engn 77 Massachusetts Ave Cambridge MA 02139 USA Penn State Univ Dept Nucl Engn 206 Hallowell Bldg University Pk PA 16802 USA Univ Michigan Dept Nucl Engn & Radiol Sci 2355 Bonisteel Blvd Ann Arbor MI 48109 USA Liberty Univ Dept Mech Engn 1971 Univ Blvd Lynchburg VA 24515 USA Argonne Natl Lab 9700 S Cass Ave Lemont IL 60439 USA Univ Missouri Dept Mech & Aerosp Engn Columbia MO 65201 USA

Oscillatory mixing of non-isothermal liquid coolant streams in advanced reactors can lead to thermal fatigue damage to fuel and reactor components. The NEAMS IRP CP2 has been seeking the development of an accurate, yet computationally affordable, turbulence modeling option for thermal-striping predictions. Efficient mixing of coolant streams in upper internal structures, lower plena, and heat exchangers significantly impacts the design of these systems, as well as their operation, and maintenance. This challenge problem generalizes specific needs related to the TerraPower and General Atomics designs by developing a set of benchmarks to advance and quantify the accuracy of the thermal striping modeling approach. The focus of the activities is to advance and demonstrate a modeling practice capable of accurately representing the performance of the structural reactor components under the influence of thermal striping. Assessment against adiabatic quasi-2D jets striping has demonstrated great promise for the proposed turbulence approaches, with 2 orders of magnitude acceleration from the reference LES solution. More recent efforts have extended the quasi-2D validation to diabatic conditions, leveraging the demonstrated accuracy of the highly resolved LES methods, and with a new set of experimental data for heated parallel round jets. Further upscaling through the use of reduced order models is being evaluated, and a two-step machine learning approach has been demonstrated on thermal striping for 3 parallel jets.

关键词： Thermal striping Parallel jets Hybrid turbulence modeling Turbulent heat transfer data driven modeling

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